class_docstr: Takes a byte-pair representation of a batch of sentences
  (as produced by the ``OpenaiTransformerBytePairIndexer``)
  and generates a `ScalarMix` of the corresponding contextual embeddings.
  
  
  
  Parameters
  ----------
  transformer: ``OpenaiTransformer``, required.
      The ``OpenaiTransformer`` module used for the embeddings.
docstr: The last dimension of the output, not the shape.
docstr_tok: ["The","last","dimension","of","the","output",",","not","the","shape","."]
code: def get_output_dim(self):
      """
      The last dimension of the output, not the shape.
      """
      return self._transformer.embed.embedding_dim
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/token_embedders/openai_transformer_embedder.py
imports: from __future__ import absolute_import
  import torch
  from allennlp.modules.openai_transformer import OpenaiTransformer
  from allennlp.modules.scalar_mix import ScalarMix
  from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
  from allennlp.nn.util import get_range_vector, get_device_of
label_desc: Use image node_id 2 for calling the OpenaiTransformerEmbedder obj's underlying member method code with example usage: obj.get_output_dim() and returns: self